code: def cumsum(a, axis=None, dtype=None, out=None):
      """
      Return the cumulative sum of the elements along a given axis.
  
      Parameters
      ----------
      a : array_like
          Input tensor.
      axis : int, optional
          Axis along which the cumulative sum is computed. The default
          (None) is to compute the cumsum over the flattened tensor.
      dtype : dtype, optional
          Type of the returned tensor and of the accumulator in which the
          elements are summed.  If `dtype` is not specified, it defaults
          to the dtype of `a`, unless `a` has an integer dtype with a
          precision less than that of the default platform integer.  In
          that case, the default platform integer is used.
      out : Tensor, optional
          Alternative output tensor in which to place the result. It must
          have the same shape and buffer length as the expected output
          but the type will be cast if necessary. See `doc.ufuncs`
          (Section "Output arguments") for more details.
  
      Returns
      -------
      cumsum_along_axis : Tensor.
          A new tensor holding the result is returned unless `out` is
          specified, in which case a reference to `out` is returned. The
          result has the same size as `a`, and the same shape as `a` if
          `axis` is not None or `a` is a 1-d tensor.
  
  
      See Also
      --------
      sum : Sum tensor elements.
  
      trapz : Integration of tensor values using the composite trapezoidal rule.
  
      diff :  Calculate the n-th discrete difference along given axis.
  
      Notes
      -----
      Arithmetic is modular when using integer types, and no error is
      raised on overflow.
  
      Examples
      --------
      >>> import mars.tensor as mt
  
      >>> a = mt.array([[1,2,3], [4,5,6]])
      >>> a.execute()
      array([[1, 2, 3],
             [4, 5, 6]])
      >>> mt.cumsum(a).execute()
      array([ 1,  3,  6, 10, 15, 21])
      >>> mt.cumsum(a, dtype=float).execute()     # specifies type of output value(s)
      array([  1.,   3.,   6.,  10.,  15.,  21.])
  
      >>> mt.cumsum(a,axis=0).execute()      # sum over rows for each of the 3 columns
      array([[1, 2, 3],
             [5, 7, 9]])
      >>> mt.cumsum(a,axis=1).execute()      # sum over columns for each of the 2 rows
      array([[ 1,  3,  6],
             [ 4,  9, 15]])
  
      """
      a = astensor(a)
      if dtype is None:
          dtype = np.empty((1,), dtype=a.dtype).cumsum().dtype
      op = TensorCumsum(axis=axis, dtype=dtype)
      return op(a, out=out)
  
file_path: mars/mars/tensor/reduction/cumsum.py
imports: import numpy
  from ...None import opcodes
  from ..datasource import tensor
  from ..arithmetic.add import TensorTreeAdd
  from .core import TensorCumReduction, TensorCumReductionMixin
label_desc: Use image node_id 1 for calling a global function with example usage: cumsum(a, axis, dtype, out) and returns: op